Welcome to the latest installment of our blog series “My Path to Google.” These are real stories from Googlers, interns and alumni highlighting how they got to Google, what their roles are like and even some tips on how to prepare for interviews.
Today’s post is all about Awa Dieng, an AI Resident on the Google Brain team in our Ghana office. Awa shares her path to working in research and machine learning at Google and how her work ensures AI systems are beneficial for everyone. If you’re interested in learning more, applications for the Google AI Residency will open in early 2021.
What first sparked your interest in working in research?
I was born and raised in Kaolack, Senegal, a country in West Africa. In school, I was always drawn to science in general and mathematics in particular. After high school, I received a government scholarship to study in France, where I received a broad education in math, physics and computer science.
As a student specializing in applied math, I started to get interested in the field of artificial intelligence (AI). I was excited about the possibilities surrounding emerging AI and machine learning (ML), and given my background and interests, research in ML seemed like a great fit.
So I pursued my first research experience—interning with the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) team at Cornell University. I also worked on ML research in an academic setting at Duke University, but I was looking to diversify my experience by working in an industry research lab, which led me to apply to work at Google.
Of course, I was aware of the Google Brain team, which is highly respected in the community and publishes important work at all major ML conferences. The AI residency seemed like the perfect opportunity to learn from these researchers and explore different areas of machine learning.
How would you describe your role at Google?
I work on the Brain team as an AI resident. The Google AI Residency is a year-long program designed to train and support the next generation of deep learning researchers. My time is spent identifying interesting problems in machine learning and working with my collaborators to solve them. This includes reading the existing literature on ML, running experiments and writing papers.
Specifically, my research is centered around machine learning and causality, which aims at identifying cause and effect and answering “what if” questions. Indeed, while machine learning has led to a lot of progress in recent years, its widespread use has highlighted issues regarding bias, reliability and transparency. These are particularly important when ML systems are used to make consequential decisions that impact people’s lives. I believe a causal perspective can address these failures, and my work aims to draw strength from these two fields to build better decision-making systems.